Skin Lesion Segmentation Detection Using U-Net Architecture

Abstract

For the early detection and precise treatment of a variety of dermatological disorders, skin lesion segmentation is essential in computer-aided diagnosis systems. The U-Net architecture, a deep learning model renowned for its remarkable performance in picture segmentation tasks, is used in this research to suggest a skin lesion segmentation strategy. In order to successfully collect fine-grained features and contextual information for precise skin lesion segmentation, our proposed method makes use of the encoder-decoder architecture of the U-Net. On publicly accessible benchmark datasets, we assess our method's performance and contrast it with cutting-edge techniques. Experimental results show that our proposed method is superior, segmenting skin lesions with excellent accuracy, precision, and recall.